Critical Solution Research Articles

Hybrid and Electric Vehicle (EV) integration in public transport: Challenges and opportunities

As cities worldwide grapple with the dual challenges of increasing urbanization and climate change, the integration of hybrid and electric vehicles (EVs) into public transport systems emerges as a critical solution. This article examines the multifaceted challenges and opportunities associated with adopting hybrid and EV technologies for buses and coaches. Key challenges include the high initial costs, the need for robust infrastructure, and concerns regarding vehicle reliability. Conversely, the transition offers significant benefits, such as substantial emissions reductions, improved operational efficiency, and enhanced public perception of sustainable practices. Through an evaluation of successful case studies from cities like London, Amsterdam, and Shenzhen, this article highlights effective strategies and outcomes achieved in the realm of public transport electrification. Ultimately, the findings underscore the necessity for collaborative efforts among stakeholders to drive the successful integration of hybrid and EV technologies, paving the way for a more sustainable urban mobility future.

Parametric investigation and scale testing on accelerated CO2 sequestration of cement-based materials by utilizing industrial waste heat

The increasing CO2 emissions from heavy industries have presented a growing threat to the global environment. The critical solution is to develop CO2 sequestration technologies, especially the fast speed CO2 fixation methods. In this study, the CO2 uptake performances of hydrated cement paste under various environment parameters coupling with high temperature were evaluated through accelerated carbonation experiments. The results show that high temperature is an effective way to improve CO2 sequestration efficiency, in which the waste heat accompanied by the emitted CO2 can be used as the source of high temperature. Based on this, appropriate vapor and liquid water addition during carbonation can further improve the CO2 sequestration performance of powder samples. Moreover, the hydration and carbonation products and the porosity before and after carbonation were evaluated, high temperature and vapor acceleration method facilitated aragonite generation and higher porosity reduction. Scale-up test of recycled aggregates on CO2 sequestration under optimum carbonation conditions was evaluated, and the results show the CO2 sequestration performance of recycled aggregates was stable in different input scales, which could sustain the CO2 sequestration amount of 80kg/t-cem.

A Comprehensive Review of Traditional and Deep-Learning-Based Defogging Algorithms

Images captured under adverse weather conditions often suffer from blurred textures and muted colors, which can impair the extraction of reliable information. Image defogging has emerged as a critical solution in computer vision to enhance the visual quality of such foggy images. However, there remains a lack of comprehensive studies that consolidate both traditional algorithm-based and deep learning-based defogging techniques. This paper presents a comprehensive survey of the currently proposed defogging techniques. Specifically, we first provide a fundamental classification of defogging methods: traditional techniques (including image enhancement approaches and physical-model-based defogging) and deep learning algorithms (such as network-based models and training strategy-based models). We then delve into a detailed discussion of each classification, introducing several representative image fog removal methods. Finally, we summarize their underlying principles, advantages, disadvantages, and give the prospects for future development.

Integrated Nutrient Management of Fruits, Vegetables, and Crops through the Use of Biostimulants, Soilless Cultivation, and Traditional and Modern Approaches—A Mini Review

The increasing population, its requirements for food, and the environmental impact of the excessive use of inputs make crop production a pressing challenge. Integrated nutrient management (INM) has emerged as a critical solution by maximizing nutrient availability and utilization for crops and vegetables. This review paper highlights the potential benefits of INM for various vegetables and field crops and explores the conceptual strategies, components, and principles underlying this approach. Studies have shown that a wide range of vegetables and field crops benefit from INM, in terms of increased yield and improvements in yield attributes, nutrient contents and uptake, growth parameters, and various physiological and biochemical characteristics. This paper discusses biostimulants, their categories, and their impact on plant propagation, growth, photosynthesis, seed germination, fruit set, and quality. Additionally, this review explores modern sustainable soilless production techniques such as hydroponics, aeroponics, and aquaponics. These cultivation methods highlight the advancements of controlled-environment agriculture (CEA) and its contribution to nutrient management, food security and minimizing the environmental footprint. The review concludes by proposing methods and fostering discussions on INM’s future development, while acknowledging the challenges associated with its adoption. Finally, this review emphasizes the substantial evidence supporting INM as a novel and ecologically sound strategy for achieving sustainable agricultural production worldwide.

Recent Breakthroughs and Future Potential of Inorganic Nanocomposites in Hydrogen Generation

AbstractGreen hydrogen synthesis has attracted researchers due to energy resource applications. Non‐renewable energy sources have been found to be incapable of meeting energy demands. Therefore, the need of the hour is to introduce an alternative to produce an efficient amount of H2. This review has summarized and investigated hetero‐catalysts and their hydrogen production potential under different light illuminations. Here, the syntheses of various nanoparticles (NPs) and composite semiconductors have been reviewed, and insights into the enhanced ability of noble‐free metal electrocatalysts have been provided. Specific nanomaterials, such as nickel, ruthenium, silver, boron‐doped SnO2, AgVO3, cobalt‐ferrite, and cobalt, dominate in synthesizing durable electrocatalysts. These exhibit Schottky junctions and enhance catalytic behaviour for hydrogen production. Finally, the challenges of the nanoparticle‘s structural and conductive properties and critical solutions are discussed. This review discusses mechanistic insights, interactions, and kinetics that involve a higher H2 synthesis rate.

A systematic review of trustworthy artificial intelligence applications in natural disasters

Artificial intelligence (AI) holds significant promise for advancing natural disaster management through the use of predictive models that analyze extensive datasets, identify patterns, and forecast potential disasters. These models facilitate proactive measures such as early warning systems (EWSs), evacuation planning, and resource allocation, addressing the substantial challenges associated with natural disasters. This study offers a comprehensive exploration of trustworthy AI applications in natural disasters, encompassing disaster management, risk assessment, and disaster prediction. This research is underpinned by an extensive review of reputable sources, including Science Direct (SD), Scopus, IEEE Xplore (IEEE), and Web of Science (WoS). Three queries were formulated to retrieve 981 papers from the earliest documented scientific production until February 2024. After meticulous screening, deduplication, and application of the inclusion and exclusion criteria, 108 studies were included in the quantitative synthesis. This study provides a specific taxonomy of AI applications in natural disasters and explores the motivations, challenges, recommendations, and limitations of recent advancements. It also offers an overview of recent techniques and developments in disaster management using explainable artificial intelligence (XAI), data fusion, data mining, machine learning (ML), deep learning (DL), fuzzy logic, and multicriteria decision-making (MCDM). This systematic contribution addresses seven open issues and provides critical solutions through essential insights, laying the groundwork for various future works in trustworthiness AI-based natural disaster management. Despite the potential benefits, challenges persist in the application of AI to natural disaster management. In these contexts, this study identifies several unused and used areas in natural disaster-based AI theory, collects the disaster datasets, ML, and DL techniques, and offers a valuable XAI approach to unravel the complex relationships and dynamics involved and the utilization of data fusion techniques in decision-making processes related to natural disasters. Finally, the study extensively analyzed ethical considerations, bias, and consequences in natural disaster-based AI.

CIPHER MAIL ANALYZER

Cryptomail Analyzer (CMA) is a critical solution that provides the ability to crack passwords and increase email security. This report details the critical role of CMAs in strengthening email security by decrypting, detecting, and identifying potential vulnerabilities in encrypted communications. Through extensive research, the report highlights the CMA's effectiveness in protecting confidentiality, ensuring integrity and mitigating the risks associated with email-based cyber threats. CMA not only detects weaknesses in the encryption process using standard encryption algorithms, but also provides an abstract solution. Additionally, CMA integrates seamlessly into existing email infrastructure, providing a non-intrusive yet powerful layer of protection against data leakage and unauthorized access. Additionally, CMA's intuitive interface and customizable features allow organizations to customize email security to specific needs and compliance requirements. Keywords: Cipher Mail Analyzer, email security, encryption analysis, confidentiality, integrity, vulnerability detection, cyber threats, encrypted communications, data protection, digital privacy, encryption protocols, remediation, infrastructure integration, customization, compliance, innovation, trust.

Implementation of the «Two-Pillar Solution» prospects in the international taxation system

The world economy has undergone a rapid digital transformation, which has caused critical discussions about the effectiveness of existing international tax systems. Traditional norms and rules are proving insufficient to solve the dynamic challenges associated with the development of the digital economy, where new sources of value creation and innovative business models challenge the established norms of profit distribution and relationships. At the same time, intangible sources of value creation contribute to tax avoidance by transnational corporations. Base Erosion and Profit Shifting (BEPS) rules are becoming a key initiative to counter these challenges. The article identifies the difficulties in reforming the modern international tax system, emphasizing the need for constant dialogue, cooperation, and adaptation to changing economic conditions. A two-step solution is an important tool in the fight against aggressive tax planning. Current discussions highlight the complex nature of the impact of the digital economy on international tax rules. Effective monitoring of the implementation and impact of the two-step solution is critical to areas for improvement, with international cooperation through the OECD playing a central role in this reform process. The BEPS rules are seen as a critical solution in the evolution of international tax policy in overcoming the challenges associated with the digital economy. The success of the implementation of the two-step solution initiative depends on effective cooperation between countries, in particular in the context of agreements on the avoidance of double taxation. The internal adaptability of this tool has the potential to improve the efficiency of the international tax system, as it directly addresses the challenges and conditions of the digital economy.

Economic evaluation of vaccination against COVID-19: A systematic review.

Coronavirus has burdened considerable expenditures on the different health systems. Vaccination programs, the critical solution against pandemic diseases, are known as safe and effective interventions to prevent and control epidemics. We aimed to perform a systematic review to provide economic evidence of the value of different types of vaccines available to combat the Covid-19 to all health policymakers worldwide. Electronic searches conducted on Medline/PubMed, Cochrane Library, Web of Science, Scopus, Embase, and other economic evaluation databases. Related and published articles searched up to March 2022 by using keywords such as "Vaccination," "Covid-19," "Cost-benefit," "Cost-utility," "Cost-effectiveness," "Economic Assessment," and "Economic evaluation." Followed by choosing the most suitable articles according to inclusion and exclusion criteria, data captured and the results extracted. The quality assessment of the articles performed by the checklist of CHEERS 2022. Finally, 13 articles included in the review. All messenger RNA vaccines were dominant with approximately 70% coverage against no vaccination in the primary vaccination program except in one study that looked at booster effects. From a payer's perspective, a dollar invested in a vaccine would be less profitable than from a societal perspective. Therefore, primary mass vaccination can be considered a cost-effective intervention in primary vaccination to save more lives and produce more positive externalities. However, the cost-benefit ratio for all vaccines increases when statistical lifetime value and global economic and educational disadvantages are considered. The COVID-19 primary vaccination programs in regional outbreaks, from a long-term perspective, will demonstrate substantial cost-effectiveness. It is suggested that due to the positive externalities of vaccination, primary mass vaccination, with the help of COVAX-19TM, could be considered a reliable way to combat viral epidemics compared to the loss of individual lives and economic and educational disturbances around the world.

Deep Learning in Carbon Neutrality Forecasting

With the growing urgency of global climate change, carbon neutrality, as a strategy to reduce greenhouse gas emissions into the atmosphere, is increasingly seen as a critical solution. However, current forecasting models still face significant challenges and limitations in accurately and effectively predicting carbon emissions and their associated effects. These challenges largely stem from the complexity of carbon emission data and the interplay of anthropogenic and natural factors. To overcome these obstacles, the authors introduce an advanced forecasting model, the SSA-Attention-BIGRU network. This model ingeniously integrates an external attention mechanism, bidirectional GRU, and SSA components, allowing it to synthesize various key factors and enhance prediction accuracy when forecasting carbon neutrality trends. Through experiments on multiple datasets, the results demonstrate that, compared to other popular methods, the SSA-Attention-BIGRU network significantly excels in prediction accuracy, robustness, and reliability.

Engaging Communities In Renewable Energy Projects For Sustainable Development

The pressing need to battle climate change and make the shift to sustainable energy sources has led to the emergence of renewable energy projects as critical solutions. While reducing the negative environmental effects of conventional fossil fuels, these programs hold up the prospect of clean, plentiful energy. However, the effective adoption of renewable energy into our world's energy system involves more than simply technological improvements; it also calls for the support and active participation of local communities. This study examines the value of community involvement and evaluates measures to strengthen local community to support renewable energy projects. With an emphasis on social acceptance, customized project design, and economic potential, the significance of incorporating local communities in the planning, implementation, and management of renewable energy projects are examined. The examination of capacity-building projects also reveals information about education and training activities, community leadership growth, technological transfer, and knowledge exchange. The advantages of combining resources, encouraging innovation, and promoting supportive policies are highlighted in the collaborative partnerships between governments, NGOs, business organizations, and local communities. Renewable energy projects may genuinely become engines for sustainable development by embracing community involvement and capacity-building, meeting local needs and aspirations while paving the path for a greener and more resilient future

Dyplomacja klimatyczna Tajwanu – w drodze do neutralności klimatycznej

TAIWAN’S CLIMATE DIPLOMACY: ON THE WAY TO CLIMATE NEUTRALITYThe aim of this paper is to present the Taiwan’s policies and actions relating to climate change. Despite the exclusion from the United Nations Framework Convention on Climate Change (UNFCCC), Taiwan has voluntarily ratified global climate conventions and has implemented a number of legislative, technical and organizational measures to reduce the emissions of greenhouse gasses and reach net zero carbon emissions by 2050. Tackling both climate change and minimizing its effects is a priority for Taiwan. In order to achieve the sustainable development goals Taiwan treats renewable energy as a critical solution in combating climate change and ensuring energy security by adding diversity to an overall electricity mix.

Nuclear Shape-Phase Transitions and the Sextic Oscillator

This review delves into the utilization of a sextic oscillator within the β degree of freedom of the Bohr Hamiltonian to elucidate critical-point solutions in nuclei, with a specific emphasis on the critical point associated with the β shape variable, governing transitions from spherical to deformed nuclei. To commence, an overview is presented for critical-point solutions E(5), X(5), X(3), Z(5), and Z(4). These symmetries, encapsulated in simple models, all model the β degree of freedom using an infinite square-well (ISW) potential. They are particularly useful for dissecting phase transitions from spherical to deformed nuclear shapes. The distinguishing factor among these models lies in their treatment of the γ degree of freedom. These models are rooted in a geometrical context, employing the Bohr Hamiltonian. The review then continues with the analysis of the same critical solutions but with the adoption of a sextic potential in place of the ISW potential within the β degree of freedom. The sextic oscillator, being quasi-exactly solvable (QES), allows for the derivation of exact solutions for the lower part of the energy spectrum. The outcomes of this analysis are examined in detail. Additionally, various versions of the sextic potential, while not exactly solvable, can still be tackled numerically, offering a means to establish benchmarks for criticality in the transitional path from spherical to deformed shapes. This review extends its scope to encompass related papers published in the field in the past 20 years, contributing to a comprehensive understanding of critical-point symmetries in nuclear physics. To facilitate this understanding, a map depicting the different regions of the nuclide chart where these models have been applied is provided, serving as a concise summary of their applications and implications in the realm of nuclear structure.

). Lessons Learned from the Pandemic in Indonesia: Enhancing Doctor-Patient Medical Communication to Improve Diagnostic Certainty in Telemedicine

Objective: The COVID-19 pandemic in Indonesia has transformed various aspects of life, particularly in healthcare, where communication between doctors and patients has shifted toward increased utilization of telemedicine. This research aims to achieve several objectives. Firstly, it investigates the deployment process of telemedicine as a remote medical service solution during pandemic circumstances, involving physical distancing and constraints. Secondly, it explores how telemedicine is adapted within clinic settings, considering the unique challenges and necessary adjustments for effective integration.
 
 Method: This research utilizes a case study approach, specifically a multiple case study.
 
 Result: Telemedicine heavily relies on patients' knowledge, information, and their role in substituting a physician's sensory input in making comprehensive medical decisions. Effective communication skills emerge as a critical solution, often overlooked as a soft skill for medical practitioners. Telemedicine encounters difficulties in discerning nonverbal cues compared to in-person consultations. To address this challenge, information gathering should encompass various media, such as photos and videos, in addition to text or verbal exchanges. Furthermore, telemedicine faces barriers related to connectivity and network issues.
 
 Conclusion: Recognizing the equal standing of doctors and patients is imperative, with physicians fostering openness and trust as the foundation for mediated communication. Although specific criteria for information collection exist, doctors should proactively explore relevant patient aspects. To mitigate network issues, healthcare facilities should establish robust network infrastructure, provide healthcare professionals with suitable devices, and consider dedicated telemedicine applications for comprehensive technology mastery.

Modeling the complexity of elliptic black hole solution in 4D using Hamiltonian Monte Carlo with stacked neural networks

In this paper, we study the black hole solution of self-similar gravitational collapse in the Einstein-axion-dilaton system for the elliptic class in four dimensions. The solution is invariant under space-time dilation, which is combined with internal SL(2,R) transformations. Due to the complex and highly nonlinear pattern of the equations of motion in the physics of black holes, researchers typically have to use various numerical techniques to make the equations tractable to estimate the parameters and the critical solutions. To this end, they have to ignore the numerical measurement errors in estimating the parameters. To our knowledge, for the first time in the literature on axion-dilation systems, we propose to estimate the critical collapse functions in a Bayesian framework. We develop a novel methodology to translate the modelling of the complexity of the elliptic black hole to a sampling problem using Hamiltonian Monte Carlo with stacked neural networks. Unlike methods in the literature, this probabilistic approach enables us not only to recover the available deterministic solution but also to explore possibly all physically distinguishable self-similar solutions that may occur due to numerical measurement errors.

SWOT analysis of electric transport and V2G implementation for power system sustainable development in the terms of Ukraine

An analysis was carried out to examine the prospects for the integration of smart charging technologies, such as Vehicle-to-Grid (V2G), into the power system in Ukraine. The country is experiencing a growing concern regarding greenhouse gas emissions. The transport sector in Ukraine is one of the most energy-intensive sectors of the economy and contributes significantly to environmental degradation. The use of internal combustion engines in transport also increases the country’s dependence on imported fuel due to the high demand for petroleum products. The integration of electric transport and smart charging technologies, particularly V2G, is seen as a critical solution with immense potential to mitigate climate change. V2G technology is becoming increasingly relevant as the world transitions towards a more sustainable energy future. V2G refers to the bi-directional flow of energy between electric vehicles (EVs) and the grid. With V2G, EVs can not only consume energy from the grid but also supply energy back to the grid during peak demand periods, helping to stabilize the grid and increase its resilience. Therefore, it is imperative to develop a stable policy to promote these technologies and compare the features of the policy of integrating electric vehicles and the energy system in Ukraine. A qualitative strategic planning methodology, Strengths, Weaknesses, Opportunities and Threats (SWOT) analysis, was used to evaluate the diverse measures and initiatives related to electric transport development and infrastructure while integrating them into the power system. This analysis identified problems associated with the impact of EV charging on the power system, such as the inability to provide increased energy needs or limiting the EV charging infrastructure load. However, a high percentage of renewable energy sources, increased social awareness of climate change, and reduced prices of electric vehicles could provide the necessary opportunities for developing electric transport in Ukraine.

Efficient application mapping approach based on grey wolf optimization for network on chip

In modern chip designs with multiple processors, network-on-chip (NoC) has emerged as a critical solution, offering scalability, flexibility, modularity, and efficiency. However, a significant challenge in application mapping is minimizing communication costs as they directly affect performance and energy consumption. Although several mapping algorithms have been developed, they often suffer from complexity, suboptimal performance, and slow convergence. To address these challenges, this paper proposes a novel approach for mapping using the grey wolf optimization (GWO) technique inspired by nature. The proposed GWO-based mapping approach for NoC (GNoC) is augmented with an effective clustering-based initial mapping strategy, further enhancing its capabilities. Furthermore, a second heuristic algorithm is introduced, incorporating a modified GWO method with polynomial regression (GNoC-PR). This modification optimizes the runtime efficiency and enhances the overall mapping quality. The proposed method was evaluated through comprehensive simulations and compared with state-of-the-art solutions. The results demonstrate that the proposed algorithm outperforms existing methods in several key metrics, including a power consumption reduction of 19.7%, energy efficiency improvement of 17%, and a significant 40% decrease in computational overhead on average. To validate the effectiveness of our mapping technique, we evaluated both embedded and synthetic applications, considering various metrics such as communication cost, average network latency, and power consumption.

Risk Perception of Vehicle-to-Vehicle Vendors and General Pedestrians: A Comparative Study

Pedestrians account for 65% of all traffic fatalities worldwide. A sub-category of pedestrians is vehicle-to-vehicle vendors, who pose a concern in countries with rapid motorization. For example, 70% of traffic fatalities in Nigeria involve general pedestrians and vendors. Previous studies have highlighted vendors’ heterogeneous road crossing and car-following behaviors. Furthermore, they create a nuisance for general pedestrians. This study contrasts the risk perception of vehicle-to-vehicle vendors and general pedestrians and analyzes grouped and ungrouped illegal crossings of vendors. A questionnaire survey was developed, based on a literature review and expert knowledge, to identify variables associated with risk perception. Interviews based on a questionnaire were conducted in various locations in Dhaka, Bangladesh, which collected 1,019 responses containing information on the respondents’ demographic attributes, risk perception, aggressive behavior, near-crash experiences, and accepted yielding distances. Next, ordinal logit/probit and complementary log-log models were employed to analyze the data. The findings revealed that vehicle-to-vehicle vendors had a lower risk perception than general pedestrians. It also indicated that vendors would take a higher risk than general pedestrians. Furthermore, vendors jaywalking alone had a significantly lower perception of risk. Finally, gender, age, education, accepted yielding distance, and aggressive behavior were the most prominent factors affecting vendors’ risk perception. Gradually separating vendors from the traffic system by shifting them to proper street markets could be a critical solution.

A general kinematic approach to the seismic stability assessment of slopes

Most of the current studies concerning slope stability assume a toe failure mechanism, which fails to capture the full complexity of slope collapse. In this study, a more comprehensive approach is developed for analyzing seismic slope stability by defining an instability behavior coefficient that considers different failure patterns, including toe, face, and base failure. The proposed coefficient is integrated with a kinematic method of limit analysis to establish a three-dimensional (3D) collapse mechanism that can describe all possible instability behaviors. The impact of earthquake forces on instability behaviors and slope stability is assessed in a modified pseudodynamic manner, which is capable of accounting for the spatiotemporal feature of seismic acceleration. A horizontal slice method is executed for calculating earthquake-induced work rates, as well as a double integral technique to compute dissipation rates due to soil resistance along the sliding surface and work rates generated by soil weight. By applying the principle of energy conservation, the lower-bound solutions of stability numbers are derived, and a further critical solution can be obtained by the combination of the Marine Predators Algorithm and the Nelder-Mead Simplex Algorithm. The proposed method is confirmed to be accurate and effective through comparisons with previous solutions. In addition, a parametric study reveals the effect of the dynamic characteristics of seismic waves on slope stability and instability behaviors. The general approach developed in this study offers a more comprehensive and accurate way to assess seismic slope stability.

The ecosystem approach to agricultural value chain finance: A framework for rural credit

In developing countries, smallholder farmers often lack long-lasting sources of credit. While traditional banking, microfinance, and cooperatives have addressed some financing gaps, Agricultural Value Chain Finance (AVCF) has attracted attention as it allows value chain actors to leverage social capital and satisfy their funding needs.To identify the driving factors for the development of AVCF, we analyze the role of non-farmer actors including banks, development organizations, agribusinesses, and academia using in-depth expert interviews. Following a Grounded Theory approach, we propose the Ecosystem Approach as a framework for establishing long-lasting AVCF schemes in developing countries based on three critical solutions: building financial platforms for value chain transaction records, implementing bundled services for the value chain, and evolving from a value chain to a value web approach. Our findings introduce six propositions that shed light on key factors for the development and longevity of financing schemes: i) the AVCF Ecosystem, ii) how AVCF schemes emerge, iii) social capital and value chain interactions, iv) culture and value chain characteristics, v) market risk, vi) transaction costs reduction. We find that AVCF can use social and trade capital to reduce transaction costs and mitigate risks related to quality, prices, and markets. In AVCF ecosystems, there are financial products for interlinked agricultural value chains with a balanced focus on all chain actors, no lock-in relationships between the lenders and the borrowers, and multiple benefits for participants. As the evolution towards an Ecosystem Approach offers a promising outlook for agricultural credit, future research should explore how policymakers and development agencies can support these schemes and how they can be used to increase financial access and equity in rural communities.